75 research outputs found
Complexity of Equivalence and Learning for Multiplicity Tree Automata
We consider the complexity of equivalence and learning for multiplicity tree
automata, i.e., weighted tree automata over a field. We first show that the
equivalence problem is logspace equivalent to polynomial identity testing, the
complexity of which is a longstanding open problem. Secondly, we derive lower
bounds on the number of queries needed to learn multiplicity tree automata in
Angluin's exact learning model, over both arbitrary and fixed fields.
Habrard and Oncina (2006) give an exact learning algorithm for multiplicity
tree automata, in which the number of queries is proportional to the size of
the target automaton and the size of a largest counterexample, represented as a
tree, that is returned by the Teacher. However, the smallest
tree-counterexample may be exponential in the size of the target automaton.
Thus the above algorithm does not run in time polynomial in the size of the
target automaton, and has query complexity exponential in the lower bound.
Assuming a Teacher that returns minimal DAG representations of
counterexamples, we give a new exact learning algorithm whose query complexity
is quadratic in the target automaton size, almost matching the lower bound, and
improving the best previously-known algorithm by an exponential factor
Learning Unions of -Dimensional Rectangles
We consider the problem of learning unions of rectangles over the domain
, in the uniform distribution membership query learning setting, where
both b and n are "large". We obtain poly-time algorithms for the
following classes:
- poly-way Majority of -dimensional rectangles.
- Union of poly many -dimensional rectangles.
- poly-way Majority of poly-Or of disjoint
-dimensional rectangles.
Our main algorithmic tool is an extension of Jackson's boosting- and
Fourier-based Harmonic Sieve algorithm [Jackson 1997] to the domain ,
building on work of [Akavia, Goldwasser, Safra 2003]. Other ingredients used to
obtain the results stated above are techniques from exact learning [Beimel,
Kushilevitz 1998] and ideas from recent work on learning augmented
circuits [Jackson, Klivans, Servedio 2002] and on representing Boolean
functions as thresholds of parities [Klivans, Servedio 2001].Comment: 25 pages. Some corrections. Recipient of E. M. Gold award ALT 2006.
To appear in Journal of Theoretical Computer Scienc
Cuts and flows of cell complexes
We study the vector spaces and integer lattices of cuts and flows associated
with an arbitrary finite CW complex, and their relationships to group
invariants including the critical group of a complex. Our results extend to
higher dimension the theory of cuts and flows in graphs, most notably the work
of Bacher, de la Harpe and Nagnibeda. We construct explicit bases for the cut
and flow spaces, interpret their coefficients topologically, and give
sufficient conditions for them to be integral bases of the cut and flow
lattices. Second, we determine the precise relationships between the
discriminant groups of the cut and flow lattices and the higher critical and
cocritical groups with error terms corresponding to torsion (co)homology. As an
application, we generalize a result of Kotani and Sunada to give bounds for the
complexity, girth, and connectivity of a complex in terms of Hermite's
constant.Comment: 30 pages. Final version, to appear in Journal of Algebraic
Combinatoric
Robustness and Generalization
We derive generalization bounds for learning algorithms based on their
robustness: the property that if a testing sample is "similar" to a training
sample, then the testing error is close to the training error. This provides a
novel approach, different from the complexity or stability arguments, to study
generalization of learning algorithms. We further show that a weak notion of
robustness is both sufficient and necessary for generalizability, which implies
that robustness is a fundamental property for learning algorithms to work
Amplifying the Security of Functional Encryption, Unconditionally
Security amplification is a fundamental problem in cryptography. In this work, we study security amplification for functional encryption (FE). We show two main results:
1) For any constant epsilon in (0,1), we can amplify any FE scheme for P/poly which is epsilon-secure against all polynomial sized adversaries to a fully secure FE scheme for P/poly, unconditionally.
2) For any constant epsilon in (0,1), we can amplify any FE scheme for P/poly which is epsilon-secure against subexponential sized adversaries to a fully subexponentially secure FE scheme for P/poly, unconditionally.
Furthermore, both of our amplification results preserve compactness of the underlying FE scheme. Previously, amplification results for FE were only known assuming subexponentially secure LWE.
Along the way, we introduce a new form of homomorphic secret sharing called set homomorphic secret sharing that may be of independent interest. Additionally, we introduce a new technique, which allows one to argue security amplification of nested primitives, and prove a general theorem that can be used to analyze the security amplification of parallel repetitions
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